Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	43.447687
daal4py_KNeighborsClassifier	0.0	2.0	51.640757
KNeighborsClassifier_kd_tree	0.0	2.0	42.632228
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	28.468151
KMeans_tall	0.0	0.0	24.135070
daal4py_KMeans_tall	0.0	0.0	8.792856
KMeans_short	0.0	0.0	3.218084
daal4py_KMeans_short	0.0	0.0	1.613673
LogisticRegression	0.0	0.0	21.181543
daal4py_LogisticRegression	0.0	0.0	4.213987
Ridge	0.0	0.0	11.421237
daal4py_Ridge	0.0	0.0	2.090994
HistGradientBoostingClassifier	0.0	5.0	26.165226
lightgbm	0.0	5.0	27.185863
xgboost	0.0	5.0	21.548497
total	0.0	59.0	17.844290

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	0.189	0.000	4.240	0.000	1	5	NaN	NaN	0.514	0.000	0.367	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	25.265	0.218	0.000	0.025	1	5	0.815	0.811	2.017	0.012	12.527	0.130	See	See
2	KNeighborsClassifier	predict	1000000	1	100	0.208	0.001	0.000	0.208	1	5	1.000	0.000	0.086	0.000	2.405	0.020	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	0.141	0.000	5.672	0.000	1	1	NaN	NaN	0.502	0.000	0.281	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	13.733	0.089	0.000	0.014	1	1	0.721	0.723	2.036	0.037	6.747	0.130	See	See
5	KNeighborsClassifier	predict	1000000	1	100	0.194	0.001	0.000	0.194	1	1	1.000	0.000	0.088	0.001	2.209	0.037	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	0.143	0.000	5.611	0.000	-1	5	NaN	NaN	0.500	0.000	0.285	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	35.880	0.000	0.000	0.036	-1	5	0.815	0.943	2.105	0.020	17.047	0.159	See	See
8	KNeighborsClassifier	predict	1000000	1	100	0.180	0.012	0.000	0.180	-1	5	1.000	1.000	0.086	0.001	2.083	0.142	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	0.141	0.000	5.677	0.000	-1	100	NaN	NaN	0.502	0.000	0.281	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	36.039	0.000	0.000	0.036	-1	100	0.944	0.811	2.037	0.026	17.690	0.224	See	See
11	KNeighborsClassifier	predict	1000000	1	100	0.190	0.019	0.000	0.190	-1	100	1.000	0.000	0.088	0.004	2.147	0.233	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	0.135	0.000	5.930	0.000	-1	1	NaN	NaN	0.502	0.000	0.269	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	25.257	0.185	0.000	0.025	-1	1	0.721	0.943	2.120	0.024	11.912	0.160	See	See
14	KNeighborsClassifier	predict	1000000	1	100	0.186	0.020	0.000	0.186	-1	1	1.000	1.000	0.087	0.001	2.154	0.233	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	0.135	0.000	5.938	0.000	1	100	NaN	NaN	0.500	0.000	0.269	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	24.614	0.162	0.000	0.025	1	100	0.944	0.723	2.017	0.015	12.203	0.123	See	See
17	KNeighborsClassifier	predict	1000000	1	100	0.202	0.001	0.000	0.202	1	100	1.000	0.000	0.088	0.003	2.300	0.070	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.264	0.000	1	5	NaN	NaN	0.114	0.000	0.532	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	22.151	0.040	0.000	0.022	1	5	0.989	0.983	0.318	0.004	69.713	0.804	See	See
20	KNeighborsClassifier	predict	1000000	1	2	0.021	0.000	0.000	0.021	1	5	1.000	1.000	0.006	0.001	3.252	0.524	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.262	0.000	1	1	NaN	NaN	0.113	0.000	0.540	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	10.562	0.024	0.000	0.011	1	1	0.983	0.973	0.319	0.004	33.136	0.431	See	See
23	KNeighborsClassifier	predict	1000000	1	2	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.006	0.001	2.434	0.343	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.261	0.000	-1	5	NaN	NaN	0.113	0.000	0.542	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	33.254	0.000	0.000	0.033	-1	5	0.989	0.986	0.371	0.001	89.541	0.309	See	See
26	KNeighborsClassifier	predict	1000000	1	2	0.026	0.001	0.000	0.026	-1	5	1.000	1.000	0.007	0.001	3.926	0.491	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.263	0.000	-1	100	NaN	NaN	0.113	0.000	0.537	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	33.401	0.000	0.000	0.033	-1	100	0.991	0.983	0.317	0.002	105.423	0.736	See	See
29	KNeighborsClassifier	predict	1000000	1	2	0.026	0.001	0.000	0.026	-1	100	1.000	1.000	0.007	0.000	3.874	0.233	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.261	0.000	-1	1	NaN	NaN	0.115	0.000	0.532	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	21.879	0.097	0.000	0.022	-1	1	0.983	0.986	0.372	0.001	58.826	0.291	See	See
32	KNeighborsClassifier	predict	1000000	1	2	0.020	0.001	0.000	0.020	-1	1	1.000	1.000	0.006	0.001	3.198	0.426	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.261	0.000	1	100	NaN	NaN	0.113	0.000	0.542	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	22.419	0.040	0.000	0.022	1	100	0.991	0.973	0.315	0.002	71.148	0.576	See	See
35	KNeighborsClassifier	predict	1000000	1	2	0.021	0.001	0.000	0.021	1	100	1.000	1.000	0.006	0.001	3.300	0.558	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.234	0.000	0.025	0.000	1	100	NaN	NaN	0.743	0.000	4.353	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	4.827	0.023	0.000	0.005	1	100	0.973	0.978	0.187	0.003	25.862	0.394	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.013	0.001	0.000	0.013	1	100	1.000	1.000	0.001	0.000	24.869	8.930	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.227	0.000	0.025	0.000	1	1	NaN	NaN	0.729	0.000	4.426	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.762	0.008	0.000	0.001	1	1	0.965	0.975	0.553	0.007	1.378	0.022	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	1	1.000	1.000	0.001	0.000	2.117	0.785	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.286	0.000	0.024	0.000	1	5	NaN	NaN	0.719	0.000	4.567	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.469	0.011	0.000	0.001	1	5	0.971	0.975	0.548	0.006	2.681	0.034	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	5	1.000	1.000	0.001	0.000	4.096	1.598	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.236	0.000	0.025	0.000	-1	100	NaN	NaN	0.759	0.000	4.263	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.644	0.026	0.000	0.003	-1	100	0.973	0.978	0.183	0.003	14.415	0.282	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.016	0.001	0.000	0.016	-1	100	1.000	1.000	0.000	0.000	32.276	12.748	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.288	0.000	0.024	0.000	-1	1	NaN	NaN	0.713	0.000	4.612	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.428	0.003	0.000	0.000	-1	1	0.965	0.954	0.100	0.001	4.295	0.039	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.000	0.000	12.541	5.624	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.251	0.000	0.025	0.000	-1	5	NaN	NaN	0.736	0.000	4.415	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.808	0.009	0.000	0.001	-1	5	0.971	0.954	0.102	0.002	7.919	0.150	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	0.000	0.006	-1	5	1.000	1.000	0.000	0.000	17.122	5.982	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.857	0.000	0.019	0.000	1	100	NaN	NaN	0.500	0.000	1.714	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.056	0.001	0.000	0.000	1	100	0.990	0.981	0.001	0.001	42.875	17.952	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.704	3.955	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.840	0.000	0.019	0.000	1	1	NaN	NaN	0.497	0.000	1.690	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.025	0.001	0.001	0.000	1	1	0.981	0.983	0.007	0.001	3.481	0.412	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.292	3.533	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.852	0.000	0.019	0.000	1	5	NaN	NaN	0.487	0.000	1.749	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.028	0.001	0.001	0.000	1	5	0.991	0.983	0.007	0.001	3.711	0.589	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.307	3.541	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.859	0.000	0.019	0.000	-1	100	NaN	NaN	0.529	0.000	1.623	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.049	0.001	0.000	0.000	-1	100	0.990	0.981	0.001	0.000	39.117	14.524	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	20.413	14.102	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.857	0.000	0.019	0.000	-1	1	NaN	NaN	0.506	0.000	1.695	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.029	0.001	0.001	0.000	-1	1	0.981	0.972	0.001	0.000	27.264	7.324	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	21.759	15.193	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.836	0.000	0.019	0.000	-1	5	NaN	NaN	0.497	0.000	1.682	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.030	0.001	0.001	0.000	-1	5	0.991	0.972	0.001	0.000	38.087	10.719	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	21.173	14.721	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.668	0.0	0.719	0.000	random	NaN	30	NaN	0.492	0.0	1.356	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.320	0.000	random	0.001	30	0.001	0.000	0.0	9.735	5.627	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.552	6.480	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.643	0.0	0.746	0.000	k-means++	NaN	30	NaN	0.460	0.0	1.398	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.330	0.000	k-means++	0.001	30	0.001	0.000	0.0	7.688	3.712	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.752	5.781	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.464	0.0	3.713	0.000	random	NaN	30	NaN	3.080	0.0	2.099	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.081	0.000	random	0.002	30	0.002	0.000	0.0	6.235	2.958	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.017	0.001	random	1.000	30	1.000	0.000	0.0	9.640	5.355	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.474	0.0	3.707	0.000	k-means++	NaN	30	NaN	2.933	0.0	2.207	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.075	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.948	2.513	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.017	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.551	6.446	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.096	0.0	0.033	0.000	random	NaN	20	NaN	0.102	0.0	0.942	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.161	0.000	random	-0.000	20	0.001	0.001	0.0	2.820	0.523	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	10.194	5.932	See	See
3	KMeans_short	fit	10000	10000	2	20	0.274	0.0	0.012	0.000	k-means++	NaN	20	NaN	0.035	0.0	7.903	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.151	0.000	k-means++	0.001	20	0.000	0.001	0.0	2.703	0.527	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.156	5.844	See	See
6	KMeans_short	fit	10000	10000	100	20	0.229	0.0	0.700	0.000	random	NaN	20	NaN	0.393	0.0	0.582	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.619	0.000	random	0.316	20	0.326	0.001	0.0	2.073	0.410	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	random	1.000	20	1.000	0.000	0.0	8.606	4.678	See	See
9	KMeans_short	fit	10000	10000	100	20	0.677	0.0	0.236	0.000	k-means++	NaN	20	NaN	0.150	0.0	4.508	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.361	0.000	k-means++	0.308	20	0.326	0.001	0.0	2.172	0.472	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.986	3.806	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.829	0.0	[-0.09974773]	0.000	NaN	NaN	NaN	NaN	NaN	1.975	0.0	5.990	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[45.56666275]	0.000	NaN	NaN	NaN	NaN	0.534	0.000	0.0	0.905	0.391	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.18062564]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.447	0.347	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.831	0.0	[2.50173673]	0.001	NaN	NaN	NaN	NaN	NaN	0.723	0.0	1.150	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[115.37611365]	0.000	NaN	NaN	NaN	NaN	0.350	0.003	0.0	0.559	0.097	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[19.66137071]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.138	0.089	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.195	0.0	0.411	0.0	NaN	NaN	NaN	0.195	0.000	0.996	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.0	7.575	0.0	NaN	NaN	0.114	0.017	0.001	0.613	0.031	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.069	0.0	NaN	NaN	NaN	0.000	0.000	0.568	0.487	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.459	0.0	0.548	0.0	NaN	NaN	NaN	0.261	0.000	5.581	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	2.610	0.0	NaN	NaN	1.000	0.000	0.000	1.202	0.685	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.000	0.637	0.611	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}